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Updated: May 30, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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深度学习驱动的晶片缺陷细分和分类.

Rohan Ingle1, Aniket K Shahade1, Mayur Gaikwad1

  • 1Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, Maharashtra, India.

MethodsX
|January 29, 2025
PubMed
概括
此摘要是机器生成的。

使用深度学习对晶片的自动缺陷检测显著提高了集成电路的质量. 这项研究实现了精确的缺陷细分和分类,简化了制造过程.

关键词:
深度学习是一种深度学习.缺陷细分 缺陷细分 缺陷细分图像细分 图像细分 图像细分集成电路 集成电路 集成电路质量管理质量管理.晶片是一种晶片.使用深度学习进行晶圆缺陷细分和分类.晶圆缺陷 晶圆缺陷 晶圆缺陷

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科学领域:

  • 半导体制造业 半导体制造业
  • 人工智能在质量控制中的作用

背景情况:

  • 集成电路 (IC) 依赖于晶片,这些晶片在加工过程中容易出现缺陷.
  • 手动检测缺陷是劳动密集型,耗时,并阻碍了高效的质量控制.

研究的目的:

  • 利用深度学习开发一种用于晶片上的缺陷细分和分类的自动化系统.
  • 整合一个大型语言模型 (LLM) 进行交互式缺陷分析和指导.

主要方法:

  • 实现了深度学习模型,用于自动缺陷细分和分类.
  • 使用的指标包括平均绝对误差 (MAE),根平均二次误差 (RMSE),子指数 (DSC),交叉与联盟 (IoU),准确性,精度,回忆和F1分数.
  • 集成了一个大型语言模型 (LLM) 作为一个问答界面,以解决与缺陷相关的查询.

主要成果:

  • 该细分模型实现了MAE为0.0036,RMSE为0.0576,DSC为0.7731,IOU为0.6590. 这两种模式的MAE为0.0036,RMSE为0.0576,DSC为0.7731,IOU为0.6590.
  • 该分类模型表现出高性能,准确度为0.9705,精度为0.9678,回忆力为0.9705,F1分数为0.9676.
  • 成功识别高强度缺陷区域后处理.

结论:

  • 深度学习有效地自动化了IC制造中的缺陷细分和分类.
  • 集成的LLM增强了用户交互,并提供了有价值的缺陷分析.
  • 这种自动化方法显著提高了最终产品的质量和制造效率.